Polynomial_Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Polynomial_Regression.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM7VusRAoPzAPuXj1C9B/0a",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/PRAN20/Polynomial_Regression/blob/main/Polynomial_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing Header Files**"
      ],
      "metadata": {
        "id": "WY-pJds1mKvy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "OU7IcI4Ql1B0"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as ps\n",
        "import matplotlib.pyplot as mt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing Datasets**"
      ],
      "metadata": {
        "id": "VO7NihhYmZ-p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = ps.read_csv('Position_Salaries.csv')\n",
        "x = dataset.iloc[:, 1:-1].values\n",
        "y = dataset.iloc[ :, -1].values"
      ],
      "metadata": {
        "id": "7D4Z7N_bmdAy"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Printing Datasets**"
      ],
      "metadata": {
        "id": "rMEmW-F6nKeJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "II2wO8winPTN",
        "outputId": "be1e62f4-1302-4e13-f995-2e8c2ce91811"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1]\n",
            " [ 2]\n",
            " [ 3]\n",
            " [ 4]\n",
            " [ 5]\n",
            " [ 6]\n",
            " [ 7]\n",
            " [ 8]\n",
            " [ 9]\n",
            " [10]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e2jUOupmnSdn",
        "outputId": "81fad2c9-99a8-4762-dd2c-3a570107af8f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[  45000   50000   60000   80000  110000  150000  200000  300000  500000\n",
            " 1000000]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Training Linear Regression Model**"
      ],
      "metadata": {
        "id": "y0IB9XAlrZaB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lin_reg = LinearRegression()\n",
        "lin_reg.fit(x, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kjd7UVDtre3M",
        "outputId": "cbb0245f-b802-41bf-a8ce-7cc011c9f3e1"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Training Polynomial Regression Model**"
      ],
      "metadata": {
        "id": "oaeT_kr4tciN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_reg = PolynomialFeatures(degree = 4)\n",
        "x_poly = poly_reg.fit_transform(x)\n",
        "lin_reg2 = LinearRegression()\n",
        "lin_reg2.fit(x_poly, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OzTDQ_nJthlq",
        "outputId": "12d5b3f2-b8b7-4f49-c6ea-d047ac94d3d2"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualising the Required values on Chart**"
      ],
      "metadata": {
        "id": "QfSOLnTUxEQY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mt.scatter(x, y , color = 'red')\n",
        "mt.plot(x, lin_reg.predict(x), color = 'blue')\n",
        "mt.title('Truth or Bluff')\n",
        "mt.xlabel('Position Level')\n",
        "mt.ylabel('Salary')\n",
        "mt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "DpN6BAKyxV0N",
        "outputId": "9e72c56a-20c0-4a34-88c7-9c711505b6fa"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualising Polynomial Regression**"
      ],
      "metadata": {
        "id": "JWvDwkmm2ysv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mt.scatter(x, y , color = 'red')\n",
        "mt.plot(x, lin_reg2.predict(poly_reg.fit_transform(x)), color = 'blue')\n",
        "mt.title('Polynomial Regression Truth or Bluff')\n",
        "mt.xlabel('Position Level')\n",
        "mt.ylabel('Salary')\n",
        "mt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "JRdkSmLU241L",
        "outputId": "e5ba86eb-3c59-4a8d-b2f8-25a36e352e25"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Making the Curve more Presentable**"
      ],
      "metadata": {
        "id": "MUQjkvMS_IOf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_grid = np.arange(min(x), max(x), 0.1)\n",
        "X_grid = X_grid.reshape((len(X_grid), 1))\n",
        "mt.scatter(x, y, color = 'red')\n",
        "mt.plot(X_grid, lin_reg2.predict(poly_reg.fit_transform(X_grid)), color = 'blue')\n",
        "mt.title('Truth or Bluff (Polynomial Regression)')\n",
        "mt.xlabel('Position level')\n",
        "mt.ylabel('Salary')\n",
        "mt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "1l1HEnx__PBH",
        "outputId": "107bcb90-6116-4ee3-c5dc-b28638ae2b6e"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predicting the Salary using Linear Regression**"
      ],
      "metadata": {
        "id": "7jiLxjGo_pVl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lin_reg.predict([[6.5]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TzBA6RdN__dC",
        "outputId": "acf1ec16-1d50-4245-be99-a20bc3898e87"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([330378.78787879])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predicting the Salary using Polynomial Regression**"
      ],
      "metadata": {
        "id": "4BKhCUdWFDjs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lin_reg2.predict(poly_reg.fit_transform([[6.5]]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XahtQxvnFLYw",
        "outputId": "e77fc988-6125-4e1c-910a-0b4ef2aaa793"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([158862.45265155])"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    }
  ]
}